Search-Based Interaction For Conversation Recommendation via Generative Reward Model Based Simulated User
Journal:
arXiv
Published Date:
Apr 29, 2025
Abstract
Conversational recommendation systems (CRSs) use multi-turn interaction to
capture user preferences and provide personalized recommendations. A
fundamental challenge in CRSs lies in effectively understanding user
preferences from conversations. User preferences can be multifaceted and
complex, posing significant challenges for accurate recommendations even with
access to abundant external knowledge. While interaction with users can clarify
their true preferences, frequent user involvement can lead to a degraded user
experience.
To address this problem, we propose a generative reward model based simulated
user, named GRSU, for automatic interaction with CRSs. The simulated user
provides feedback to the items recommended by CRSs, enabling them to better
capture intricate user preferences through multi-turn interaction. Inspired by
generative reward models, we design two types of feedback actions for the
simulated user: i.e., generative item scoring, which offers coarse-grained
feedback, and attribute-based item critique, which provides fine-grained
feedback. To ensure seamless integration, these feedback actions are unified
into an instruction-based format, allowing the development of a unified
simulated user via instruction tuning on synthesized data. With this simulated
user, automatic multi-turn interaction with CRSs can be effectively conducted.
Furthermore, to strike a balance between effectiveness and efficiency, we draw
inspiration from the paradigm of reward-guided search in complex reasoning
tasks and employ beam search for the interaction process. On top of this, we
propose an efficient candidate ranking method to improve the recommendation
results derived from interaction. Extensive experiments on public datasets
demonstrate the effectiveness, efficiency, and transferability of our approach.
